Of course, repo.bzr is your old bzr repository here, while repo.git is your newly created git repository. The fast-export/fast-import commands convert the repository’s history from bzr to git. To populate our directory with the bzr repository’s files, however, we also need to perform a hard reset. If you need to convert multiple bzr branches to git, have a look at this post.

I started experimenting with distributed version control systems (DVCS) while working on my MSc thesis (around 2005). I originally maintained Cassowary.net using darcs (a DVCS written in Haskell), and switched to bzr later. Back in the early days of Uiml.net, we used CVS, which was horrible at moving or renaming files, not to mention the inability to work offline and still commit your work.

I preferred darcs and bzr over git at the time because they were a lot easier to use. These days, git seems to have catched up to bzr in that regard. Back in 2006, I also did a few performance tests and found that bzr was a lot slower than git. Things seem to have improved somewhat, but git is still The King of Speed. Oh, and GitHub is great!

Although I already created a Facade project page a while ago and made the code available through Bazaar, I never announced it here. To make it easier to try the script out, I also uploaded an archive of the code. This version includes a simple Python script that works on Windows (simple_winclient.py) that performs face detection.

I noticed today that Jinwei Gu, a PhD student at the University of Columbia and teaching assistant for the course COMS W4737 (E6737) Biometrics, included my blog post in a list of links to resources for doing face detection.

Last year, two students improved the presence detection in Facade during their Bachelor’s theses.

Bram Bonné worked on the network aspect, to allow users to have their presence detected on multiple computers. When users would move from one computer to another (or to a mobile device), their messages would automatically be routed to their current machine.

Here are a few screenshots from Bram’s thesis:

Bram also created a demo video of his system:

Kristof Bamps worked on supporting a more diverse set of sensors (e.g. presence of a Bluetooth phone, keyboard and mouse input). He used a decision tree to model all these sensors, and determine the user’s correct presence based on the collected information.

Two screenshots from Kristof’s thesis:

Kristof’s demo video:

I have unfortunately not yet found the time yet to merge Kristof’s and Bram’s code into the main Facade branch, but this should not be too big of a problem.

Just a quick update on my previous post. I hooked my code up to a D-Bus daemon, and used it to detect if I was sitting behind my desk or not.

I had to position the camera a bit lower, because it would sometimes not recognize my face when I was looking at the bottom of my screen. Having a dual-monitor setup doesn’t help either, since you should ideally position the webcam in the middle of the two displays to have good coverage. I positioned the webcam in the middle of the two displays at the bottom and tilted it upwards, which gives good results:

I guesstimated that if the system couldn’t detect a face for a period of 10 seconds, I would be away from my desk (or at least not paying attention to the screen). I played around a bit with pygtk and notify-python to create a status icon and show notification bubbles whenever my status changed. Here’s what happens when it detects that I’m away from my desk (notice the bubble in the upper right corner):

When I return, the system will change its status icon accordingly and notify me again:

I had some fun with Gary Bishop’s OpenCV Python wrapper this morning. I wanted to try out OpenCV for detecting faces using a web cam. This could be used for instance to see if someone is sitting behind his desk or not. I used Gary’s Python wrapper since I didn’t want to code in C++.

A known problem is that pressing the escape key doesn’t quit the program. Might be something wrong in my use of the cv.WaitKey function. Meanwhile you can just use Ctrl+C. All in all, the face detection works pretty well. It doesn’t recognize multiple faces yet, but that might be due to the training data. It would be interesting to experiment with OpenCV’s support for eye tracking in the future.

Update: the script does recognize multiple faces in a frame. Yesterday when Alex stood at my desk, it recognized his face as well. I think it didn’t work before because I used cv.Size(100, 100) for the last parameter of cv.HaarDetectObjects instead of cv.Size(50, 50). This parameter indicates the minimum face size (in pixels). When people were standing around my desk, they were usually farther away from the camera. Their face was then probably smaller than 100×100 pixels.

Just a quick note on ctypes. I remember when I created PydgetRFID that I tried to use libphidgets’ SWIG-generated Python bindings, but couldn’t get them to work properly. I had read about ctypes, and decided to use it for creating my own wrapper around libphidgets. Within a few hours I had a working prototype. When you’re struggling with SWIG-generated Python bindings, or have some C library without bindings that you would like to use, give ctypes a try. Gary Bishop wrote about a couple of interesting ctypes tricks to make the process easier.